Remote Direct Memory Access (RDMA) offers high performance through OS kernel bypass and has become a key technology in modern data centers. By exploiting memorysemantic operations, RDMA-based data structures can achieve high scalability and significantly reduce CPU utilization compared with traditional Ethernet-based systems. However, classic sorted indexes such asSkiplistsuffer from low throughput under RDMA memory semantics due to frequent and costly remote accesses. To achieve both scalability and high throughput for a lock-based concurrent Skiplist, this paper introduces a hybrid paradigm that combines one-sided and two-sided RDMA operations. This proposal is built upon a re-evaluation of core design choices in RDMA, including transport modes, caching strategies, and memory management. Building on this paradigm, we designHybridSkipList+, a distributed Skiplist system that integrates a client-side coherent cache with pull-based synchronization to reduce expensive network round trips, and a semi-continuous memory allocator to enhance RDMA access locality. We implementHybridSkipList+ on an eight-machine RDMA cluster and conduct extensive evaluations. Results show that HybridSkipList+ outperforms two baseline systems by up to 4.41× and 3.45× under typical workload conditions.

The provision of performance-aware video conferencing services today relies on approaches that focus on data compression and client-side bitrate adaptation techniques to optimize transmission. However, these methods fail to quickly respond to fluctuations in network conditions, thereby compromising the quality of service for transmissions. For this reason, this article aims to propose a novel traffic scheduling-based video transmission optimization solution from the perspective of the network service provider. We first investigate the resource requirements of video conferences and present the experiential performance of video conferences under different network conditions and network competition. Based on these results, we design a service-customized routing mechanism called VCApather that minimizes network contention. We then provide implementation solutions for the control plane and the data plane of VCApather . We evaluate VCApather using a fully meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather could satisfy 69.8% more QoE requirements and yielded an average bitrate improvement of 1.74 \(\times\) .

Real-time communication (RTC) applications rely on accurate bandwidth estimation to ensure high-quality communication and user experience. Traditional heuristic and reinforcement learning (RL)-based methods often face challenges with the dynamic nature of real-time networks, leading to issues with generalization. Inspired by the success of Large Language Models (LLMs)---which, with billions of parameters pre-trained on massive datasets, have demonstrated exceptional capabilities in semantic representation, adaptability, and transfer learning---we propose LLM4Band, a novel framework that integrates LLMs with offline reinforcement learning to tackle bandwidth estimation in RTC scenarios. By leveraging the powerful feature extraction capabilities of LLMs and combining them with an offline RL algorithm, LLM4Band incorporates a Balanced Replay Buffer and an LLM-based policy network to significantly enhance robustness and adaptability. Extensive experiments demonstrate that LLM4Band surpasses state-of-the-art methods, achieving a 12.35% improvement in estimation accuracy and a 21% enhancement in communication quality.

We propose a network service as a solution for video conference applications by constructing network layer routing strategies. Our approach takes into account the characteristics of conferencing flows, addresses various self-customized metrics, and proactively ensures a positive user experience by preventing contention. The performance of VCApather is evaluated using a fully-meshed topology with five nodes and real-world video conference traffic. The results show that VCApather is capable of achieving high link utilization and balance, while also meeting predefined user metrics. Compared to other schemes, VCApather was found to satisfy 69.8% more QoE requirement and to yield an average bitrate improvement of 1.74×.

This document proposes a method of addressing and routing for massive Low Earth Orbit (LEO) satellite constellations. It is based on the concepts and technologies of New IP. LEO satellite networks can be used for Internet access and for the Non-Terrestrial Network (NTN) integration with 5G and beyond. Our new addressing method leverages the characteristics of satellites with known orbit elements; and simplifies the satellite identification by using limited indexes. The new routing method combines semantic addressing with source routing and generates a new semantic routing scheme. Compared with traditional methods, the new proposal dramatically reduces the workload in satellite, such as table size, TCAM (Ternary Content-addressable Memory) lookups, and packet header size. Thus, it is more suitable to network in space where the harsh environment will restrict the hardware performance, power consumption, link bandwidth and system complexity.

Reducing mankind’s carbon footprint and becoming more sustainable is rapidly becoming one of the main challenges of our age. The networking industry needs to respond to those challenges not only by enabling applications that allow users to save energy (such as, replacing travel with teleconferencing), but also by making networking technology itself significantly more energy-efficient. As a result, the aspect of minimizing network power consumption is rapidly increasing in importance. This position paper aims to articulate opportunities where energy efficiency of networks can be improved, to point out the associated technical challenges, and to identify corresponding research challenges.

Qualitative Communication has been proposed to increase the responsiveness of a network due to packet loss. The basic idea is to allow the network to drop part of the payload to preserve the integrity of the session (as opposed to dropping whole packets as in TCP). We consider this idea in the context of AR/VR and see how selectively dropping payload naturally fits with such an application where the data can be easily split within some critical and non-critical data. We present basic mechanism to leverage qualitative communications in 360 degree video streaming, as well as a pre-fetching scheme. We evaluate this proposal on actual 360 video traces and show the significant improvement of our proposal versus a vanilla transmission mechanism.

360-degree video streaming, which is becoming more and more popular as the fast development of VR/AR applications nowadays due to the immersive viewing experience it can offer, poses enormous challenges to the current network infrastructure in terms of high bandwidth and low latency requirements. To address this problem and to ensure the QoE (quality of experience) of end-users, this paper presents CUBIST, a method and system for high-quality 360-degree video streaming in networks with cache nodes at the edge. To the best of our knowledge, it is the first tile-based edge caching solution that incorporates proactive tile prefetching and hierarchical cache organization into reactive caching to maximize the caching benefit while reducing the cost of 360-degree video streaming. Experimental results show that CUBIST can achieve a cache hit ratio of 87 % and improve the effective video bitrate by 12.9 % with most rate transitions being small when compared with the latest FoV-aware edge caching scheme.

Not all data units carried over a packet stream are equal in value. Often some portions of data are more significant than others. Qualitative Communication is a paradigm that leverages the ‘quality of data’ attribute to improve both the end-user experience and the overall performance of the network specifically when adverse network conditions occur. Qualitative Communication allows that the content that is received differs from the one that is transmitted but maintains enough of its information to still be useful. This paper describes Qualitative Communication, its packetization methods, and the corresponding mechanisms to process the packets at a finer granularity with New IP. The paper also discusses its benefits to the emerging and future network applications through a couple of use cases in video streaming, multi-camera assisted remote driving, ARNR and holographic streaming, and high precision networking. Some preliminary performance results are illustrated to show such benefits. This suggests that Qualitative Communication will find wide applications for many use cases.

A new transport protocol is introduced to increase the responsiveness of the network to congestion. The new transport protocol, QUCO, reacts to congestion by selectively dropping off parts of a payload packet (combined with mitigation mechanisms to handle the loss of part of the payload). This packet trimming scheme greatly reduces the variations in the number of the packets going through the network. This allows to set tighter targets on the number of packets in flight and on the depth of the switch buffers. QUCO has less delay and much less delay variations than TCP. The resulting reduction in jitter is extremely useful, especially for media distribution.

Using local caches is becoming a necessity to alleviate bandwidth pressure on cellular links, and a number of caching approaches advocate caching popular content at nodes with high centrality, which quantifies how well connected nodes are. These approaches have been shown to outperform caching policies unrelated to node connectivity. However, caching content at highly connected nodes places poorly connected nodes with low centrality at a disadvantage: in addition to their poor connectivity, popular content is placed far from them at the more central nodes. We propose reversing the way in which node connectivity is used for the placement of content in caching networks, and introduce a Low-Centrality High-Popularity (LoCHiP) caching algorithm that populates poorly connected nodes with popular content. We conduct a thorough evaluation of LoCHiP against other centrality-based caching policies and traditional caching methods using hit rate, and hop-count to content as performance metrics. The results show that LoCHiP outperforms significantly the other methods.

Caching contents in a small cell base station (SBS) is getting supported more and more widely today due to the Internet traffic growth and the requirement of low access latency. A primary concern and challenging issue with cache-enabled SBSs is how to better utilize network resources to achieve high overall performance. Though existing caching strategies can solve the problem to some extent, they are far from perfect --- pure popularity-based ones usually lead to sub-optimal caching performance whereas global coordination ones suffer from extra cost of many control messages. To deal with the issue, we present an adaptive caching scheme based on Bayesian inference, which 1) identifies the traffic features over time for each SBS; 2) synthesizes various features to rank the contents via a Bayesian ranking model; and 3) does cache placement online according to the ranking results. Unlike existing approaches that only highlight some specific factor, our scheme, due to the adoption of a Bayesian approach, can easily support additional features of high impact on caching performance and measure them in a decentralized way within a single SBS. We evaluate our scheme under various circumstances in terms of SBSs density, cache size, content popularity and skewness. The results show that our solution using multiple features exhibits improved performance --- it can reduce more than 30% the overall network latency in some cases when compared with solutions that only use a single feature.

There has been increasing attention to virtual reality applications in recent years, especially to immersive or 360-degree videos that typically consume much more bandwidth than traditional ones. Though all produced data is transferred, only a small part (denoted as Field of View or viewport) is watched by users due to the nature of immersive videos. Obviously, this causes a large waste of network resources. Hence, it is important to define a viewport-dependent streaming transmission strategy by detecting where the user is gazing and the movement of the user's head. Unfortunately, there are few datasets providing this information. In this paper, we propose a tile-based simulation approach to generate the distribution of the user's behavior and to provide information that can be used to optimize future view-dependent streaming protocols. We first characterize the users' viewport pattern from datasets gathered from real users by decomposing the 360-degree stream into tiles and analyzing the frequency and time-interval distribution for each tile. Then, we devise a hierarchical Markov model that incorporates the beta distribution of each tile time interval to predict tile transition. The results show that the simulation tool characterizes the tile sequences of users accurately, performing close to the empirical results.

Video transcoding is widely adopted in online video services to transcode videos into multiple representations for dynamic adaptive bitrate streaming. This solution may consume significant resources and incur intolerable processing delays. Meanwhile, different videos have different quality-of-service (QoS) requirements for transcoding. Delay-sensitive videos must be transcoded within a strict deadline, whereas delay-tolerant videos are not required to be transcoded immediately. Some intelligent policies are required for provisioning the right amount of resources in the transcoding system to meet the heterogeneous QoS requirements, especially under dynamic workloads. To this end, we develop a robust dynamic priority-based resource provisioning scheme for video transcoding. We adopt the preemptive resume priority discipline to design a multiple-priority transcoding mechanism. The system performs the transcoding for delay-tolerant videos by utilizing idle resources for improving resource utilization while not affecting the transcoding for delay-sensitive videos. We adopt the model predictive control framework to design an online algorithm for dynamic resource provisioning to accommodate time-varying workloads by predicting future workloads. To seek performance robustness against prediction noise, we improve the performance of our online algorithm via robust design. The experimental results demonstrate that our proposed method can satisfy the heterogeneous QoS requirements while significantly reducing computing resource consumption.

Video delivery has been playing an essential role in video services over edge networks. Although HTTP segment-based streaming, e.g., Dynamic Adaptive Streaming over HTTP (DASH), has become the prevailing technique, it cannot provide guaranteed video playback in terms of bitrate to mobile users. In essence, HTTP streaming downloads the video segments in a best effort fashion, i.e., passively responding to the channel dynamics. This can cause unstable playback with frequent rebuffer and multi-client competition that degrades a network-wide performance. In this paper, we present a network-assisted streaming framework for Guaranteed Playback-Experience Streaming over HTTP (GESH) that leverages the proactive control of network resources and joint coordination among multiple clients for service-aware network resource allocation. Specifically, GESH is empowered by a new weighted proportional fair scheduling without modifying existing cellular infrastructure, a per-segment channel variation model, and a suite of algorithms to seek the optimal weights for the scheduling. Extensive evaluations show that GESH can maximally guarantee the video playback of multiple users, as well as significantly outperforming conventional HTTP streaming and current DASH systems.

Information-centric networking (ICN) has been touted as a revolutionary solution for the future of the Internet, which will be dominated by video traffic. This work investigates the challenge of distributing video content of adaptive bitrate (ABR) over ICN. In particular, we use the in-network caching capability of ICN routers to serve users; in addition, with the help of named function, we enable ICN routers to transcode videos to lower-bitrate versions to improve the cache hit ratio. Mathematically, we formulate this design challenge into a constrained optimization problem, which aims to maximize the cache hit ratio for service providers and minimize the service delay for endusers. We design a two-step iterative algorithm to find the optimum. First, given a content management scheme, we minimize the service delay via optimally configuring the routing scheme. Second, we maximize the cache hits for a given routing policy. Finally, we rigorously prove its convergence. Through extensive simulations, we verify the convergence and the performance gains over other algorithms. We also find that more resources should be allocated to ICN routers with a heavier request rate, and the routing scheme favors the shortest path to schedule more traffic.

In-network caching is considered to be an important solution to efficiently using network resources to achieve a high overall content delivery performance in both information-centric networks (ICNs) and 5G wireless networks. Content placement plays a key role in achieving this goal. Unfortunately, most content placement strategies today rely on opportunistic caching due to the problem complexity. We analyze the content placement problem in detail and present MCPC, a new content placement strategy for architectures that support in-network caching. Unlike existing content placement approaches that try to increase cache hit ratio, MCPC leverages the information recorded in each network node to reduce content access latency. MCPC proposes two new mechanisms: 1) a content load allocation estimation method based on local requests aggregation information; and 2) a content placement algorithm that avoids long-distance signaling messages by partitioning the network into smaller domains. We evaluate MCPC on a variety of network topologies, cache sizes and content popularity distributions. The experimental results show that MPCP can reduce by up to 56% the content access latency while providing comparable cache hit ratios as traditional benchmarks.

The exponential growth of data traffic raises a great challenge to content delivery in current TCP/IP networks. To answer this challenge, Information-Centric Networking (ICN) has been proposed with the purpose of bringing content caching and name-based content access to the network layer. Though great progress has been made, most existing ICN proposals lack support for parallel data transfer over multiple paths with low data redundancy. To deal with the issue, we present CODA, a fully distributed cooperative multipath data transmission solution that enhances content delivery further. Taking Named Data Networking (NDN) as a basis, CODA works in a distributed manner with the following contributions: 1) it extends the standard Interest model in NDN to support transmission of data over multiple paths so as to reduce the flow completion time; 2) it devises a traffic scheduling model to form parallel paths for transmitting data in a cooperative way; and 3) it proposes a transmission control scheme to select paths in an efficient and reliable manner. Extensive simulation comparisons with existing data transmission methods show that: 1) CODA speeds up the data rate twice as high as that of the best-route method; and 2) the amount of Interests required by CODA to build multiple data transmission paths in the network accounts for only 66% of that by MSRT, another multipath transmission proposal.

As network utilization keeps increasing, it is essential to have mechanisms to better schedule traffic in the network. Using content-based resource allocation allows to deliver a fine- grained scheduling and to monitor not only the instantaneous link utilization but also the backlog that is already scheduled onto a link in the future. We propose to use a manifest prior to a session to inform the session manager of the requested bandwidth. We propose to make that information available to the scheduler in the data center context. We have implemented our scheme in a testbed and demonstrate a significant benefit for a data center use case.

As networking attempts to cleanly separate the control plane and forwarding plane abstractions, it also defines a clear interface between these two layers. An underlying network state is represented as a view to act upon in the control plane. We are interested in studying some fundamental properties of this interface, both in a general framework, and in the specific case of content routing. We try to evaluate the traffic between the two planes based on allowing a minimum level of acceptable distortion in the network state representation in the control plane. We apply our framework to content distribution, and see how we can compute the overhead of maintaining the location of content in the control plane. This is of importance to evaluate resolution-based content discovery in content-oriented network architectures: we identify scenarios where the cost of updating the control plane for content routing overwhelms the benefit of fetching the nearest copy. We also show how to minimize the cost of this overhead when associating costs to peering traffic and to internal traffic for network of caches.

Transcoding is widely adopted for content adaptation; however, it may incur excessive resource consumption and processing delays. Taking advantage of cloud infrastructure, cloud-based transcoding can elastically allocate resources under time-varying workloads and perform multiple transcodings in parallel to reduce delays. To provide transcoding as a cloud service, cloud transcoding systems require some intelligent mechanisms to provision resources and schedule tasks to satisfy user requirements while maximizing financial profit. To this end, we propose a two-timescale stochastic optimization framework for maximizing service profit while achieving performance requirements by jointly provisioning resources and scheduling tasks under a hierarchical control architecture. Our method analytically integrates service revenue, processing delay, and resource consumption in one optimization framework. We derive the offline exact solution and design some approximate online solutions for task scheduling and resource provisioning. We implement an open source cloud transcoding system, called Morph, and evaluate the performance of our method in a real environment. Empirical studies verify that our method can reduce resource consumption and achieve a higher profit compared with baseline schemes.

Information-Centric Networks offer an opportunity to re-think traffic engineering, by allowing to schedule resource at the granularity of content. We describe an architecture for performing content-based path selection and resource allocation. It assigns content objects to paths in the network by considering the time it will take to deliver the existing objects in the network and optimizes with respect to this objective. We have extensively simulated this mechanism to demonstrate significant gain up to 72% in response time when compared with minimum backlog policy (MBP), round-robin format (RRF), and the traffic engineering policy which minimizes the maximum link utilization (minMLU).

The introduction of Dynamic Adaptive Streaming over HTTP (DASH) helped reduce the consumption of resources in video delivery, but its client-based rate adaptation is unable to optimally use the available end-to-end network bandwidth. We consider the problem of optimizing the delivery of video content to mobile clients while meeting the constraints imposed by the available network resources. Observing the bandwidth available in the network's two main components, core network, transferring the video from the servers to edge nodes close to the client, and the edge network, which is in charge of transferring the content to the user via wireless links, we aim to find an optimal solution by exploiting the predictability of future user requests of sequential video segments, as well as the knowledge of available infrastructural resources at the core and edge wireless networks in a given future time window. Instead of regarding the bottleneck of the end-to-end connection as our throughput, we distribute the traffic load over time and use intermediate nodes between the server and the client for buffering video content to achieve higher throughput, and ultimately significantly improve the Quality of Experience for the end user in comparison with current solutions.

As the number of devices which could be potentially connected to the Internet scales up, as the amount of traffic generated by these devices will explode, it is necessary to reconsider the underlying protocols which will support the Internet of Things. The request/response semantics of CCN are well suited to the retrieving data from a set of sensors and we consider here the deployment of CCN to retrieve missing objects in a distributed environment. We developed a demonstrator of it as a proof-of-concept. We then consider the scalability of such system and demonstrate how the combination of a Bloom Filter mechanism in addition to the CCN primitives allow for a very scalable deployment of an object retrieval function.

We consider the benefits brought by network coding in Information-Centric Networks (ICNs) in the case of a video streaming application. Network coding, when combined with ICN, allows a data transfer session to use multiple sources for the content seamlessly. It permits the client fetching the content to use multiple interfaces at the same time in an asynchronous manner, while using their capacity in an additive manner. This allows to create a logical link between the user and the content. In the case of video streaming, this logical link allows the rate adaptation logic to find the proper streaming rate while using multiple links concurrently. We implemented a video streaming system which works using network coding and CCN. We have shown that this implementation performs satisfactorily, and has comparable performance to a system without network coding in the unicast single source, single path case, and delivers significant performance gain (better QoE, higher throughput) when the video client retrieves the stream from multiple concurrent sources. We hope to demonstrate that network coding and CCN provides seamless mobility for a video streaming application.

Video transcoding is widely adopted in online video sharing services to encode video content into multiple representations. This solution, however, could consume huge amount of computing resource and incur excessive processing delays. Moreover, content has heterogeneous QoS requirements for transcoding. Some content must be transcoded in real time, while some are deferrable for transcoding. It needs to determine the strategy for intelligently provisioning the right amount of resource under dynamic workload to meet the heterogeneous QoS requirements. To this end, this paper develops a robust dynamic resource provisioning scheme for transcoding with heterogeneous QoS criteria. We adopt the Preemptive Resume Priority discipline for scheduling, so that the transcoding-deferrable content can utilize idle resources for transcoding to maximize resource utilization while remain transparent to delay-sensitive content. We leverage Model Predictive Control to design the online algorithm for dynamic resource provisioning using predictions to accommodate time-varying workload. To seek robustness of system performance against prediction noises, we improve our online algorithm through Robust Design. The experiment results in a real environment demonstrate that our proposed framework can achieve the QoS requirements while reducing 50% of resource consumption on average.

The current Internet is under tremendous pressure due to the exponential growth in bandwidth demand, fueled by the transfer of video consumption to online distribution, IPTV, streaming services such as Netflix, and from phone networks to videoconferencing and Skype-like video communications. The Internet has also democratized the creation, distribution and sharing of user-generated video contents through services such as YouTube, Vimeo or Hulu. The situation is further aggravated by the emerging trends of adopting higher definition video streams, requesting more and more bandwidth. Indeed, the Cisco Visual Networking Index (VNI) projects that video consumption will amount to 90% of the global consumer traffic by 2017. Another shift predicted by Cisco VNI is that most data communications will be wireless by 2018.

Wireless information-centric networks consider storage as one of the network primitives, and propose to cache data within the network in order to improve latency and reduce bandwidth consumption. We study the throughput capacity and latency in an information-centric network when the data cached in each node has a limited lifetime. The results show that with some fixed request and cache expiration rates, the order of the data access time does not change with network growth, and the maximum throughput order is not changing with the network growth in grid networks and is inversely proportional to the number of nodes in one cell in random networks. Comparing these values with the corresponding throughput and latency with no cache capability (throughput inversely proportional to the network size, and latency of order √n and the inverse of the transmission range in grid and random networks, respectively), we can actually quantify the asymptotic advantage of caching. Moreover, we compare these scaling laws for different content discovery mechanisms and illustrate that not much gain is lost when a simple path search is used.

Online social networks (OSNs) serve today as a platform for information dissemination. At the same time, mobile devices provide ubiquitous network access through the cellular infrastructure. In this paper, we develop mechanisms for minimizing the peak load of the cellular network due to information cascades spreading on social media. First, we exploit the social ties for predicting information dissemination and we propose Proactive Seeding-a technique for minimizing the peak load of cellular networks. Much of such a load is due to information cascades spreading in social media, and we address it by proactively pushing (“seeding”) content to selected users before they actually request it. We develop a family of algorithms that take as input information primarily about: (i) cascades on the OSN, (ii) the background traffic load in the cellular network, and (iii) the local connectivity among mobiles; the algorithms then select which nodes to seed and when. We prove that Proactive Seeding is optimal when the prediction of information cascades is perfect. We perform simulations driven by traces from Twitter and cellular networks and we find that Proactive Seeding reduces the peak cellular load by 20-50 percent. Then, we exploit the fact that there is correlation between social ties and physical proximity and we combine Proactive Seeding with device-to-device communication to further reduce the peak load.

In this paper we conduct a performance evaluation of privacy protocols for Information Centric Networking (ICN). Our contribution is three-fold: Firstly, we define a simple but complete performance framework for comparing current and future solutions. Secondly, we conjecture and prove the existence of unsafe replicas, namely cached content that remains available to users whose access has been revoked. Thirdly, we propose a performant protocol that solves the problem of unsafe replicas without tampering with the caching functionality of ICN.

This paper proposes a network-aware resource management scheme that improves the quality of experience (QoE) for adaptive video streaming in CDNs and Information-Centric Networks (ICN) in general, and Dynamic Adaptive Streaming over HTTP (DASH) in particular. By utilizing the DASH manifest, the network (by way of a logically centralized controller) computes the available link resources and schedules the chunk dissemination to edge caches ahead of the end-user's requests. Our approach is optimized for multi-rate DASH videos. We implemented our resource management scheme, and demonstrated that in the scenario when network conditions evolve quickly, our approach can maintain smooth high quality playback. We show on actual video server data and in our own simulation environment that a significant reduction in peak bandwidth of 20% can be achieved using our approach.

Mobile adaptive HTTP streaming with centralized consideration of multiple streams has gained increasing interest. It poses a special challenge that the interests of both content provider and network operator need to be deliberately balanced. More importantly, the adaptation is required to be flexible enough to be ported to various systems that work under different network environments, QoE levels, and economic objectives. To address these challenges, we propose a Markov Decision Process (MDP) based network-assisted adaptation framework, wherein cost of buffering, significant playback variation, bandwidth management and income of playback are jointly investigated. We then demonstrate its promising service provisioning and maximal profit for a mobile network in which fair or differentiated service is required.

Given the exploding growth of video traffic, efficient video distribution is essential to the future Internet. Therefore, how to optimize its networking cost is a critical research problem. In this paper, we introduce Network Function Virtualization (NFV) in conjunction with Software-Defined Networking (SDN) to minimize the cost via joint orchestration of caching, transcoding and routing functions. Specifically, we propose a two-step iterative approach. First, in NFV-based resource allocation phase, we maximize total cache hits by optimally allocating storage and computing resources for a giving routing policy. Second, in SDN-based routing phase, we minimize the networking cost by optimally configuring the routing matrix for a given resource placement. Finally, we analytically prove their iterative repeat converges to the joint optimum. Through extensive simulations, we verify its convergence, and performance gains compared with the optimal solution of either phase alone. By examining numerical results, we obtain some operational guidelines. From the resource allocation aspect, we should allocate more resources to the node with heavier request rate. From the routing aspect, for each node-server pair, the node should split the traffic across multiple paths with identical shortest hops if there are many, or use the shortest path alone if there is only one.

Nowadays, large-scale video distribution feeds a significant fraction of the global Internet traffic. However, existing content delivery networks may not be cost efficient enough to distribute adaptive video streaming, mainly due to the lack of orchestration on storage, computing, and bandwidth resources. In this paper, we leverage Media Cloud to deliver on-demand adaptive video streaming services, where those resources can be dynamically scheduled in an on-demand fashion. Our objective is to minimize the total operational cost by optimally orchestrating multiple resources. Specifically, we formulate an optimization problem, by examining a three-way tradeoff between the caching, transcoding, and bandwidth costs, at each edge server. Then, we adopt a two-step approach to analytically derive the closed-form solution of the optimal transcoding configuration and caching space allocation, respectively, for every edge server. Finally, we verify our solution throughout extensive simulations. The results indicate that our approach achieves significant cost savings compared with the existing methods used in content delivery networks. In addition, we also find the optimal strategy and its benefits can be affected by a list of system parameters, including the unit cost of different resources, the hop distance to the origin server, the Zipf parameter of users' request patterns, and the settings of different bitrate versions for one segment.

The articles in this special section focus on the evolution of software defined networking; network virtualization; and network function virtualization.

In Part I of the special issue, the main reported research contributions were: efficient resource allocation and management of softwarized network functions, design of highperformance platforms to allow network function virtualization on commodity machines, and enabling efficient collaboration between providers in softwarized networks. From the twenty six submitted papers, four papers had been selected for Part I of the special issue. An additional set of four more papers have been accepted for this Part II, after a thorough revision by the authors to take into account the detailed comments from the reviewers. The four selected papers in Part II of the special address three very important topics for the efficient management of Software-Defined Networking/Virtualized Network Functions-based (SDN/NFV-based) telecommunication systems: (i) optimizations to flow-based software-defined networks to address the scalability and energy consolidation requirements, (ii) programming abstractions in wireless software-defined networks, and (iii) improved network virtualization to more efficiently support latency sensitive applications.

Current Internet performs traffic engineering (TE) by estimating traffic matrices on a regular schedule, and allocating flows based upon weights computed from these matrices. This means the allocation is based upon a guess of the traffic in the network based on its history. Information-Centric Networks on the other hand provide a finer-grained description of the traffic: a content between a client and a server is uniquely identified by its name, and the network can therefore learn the size of different content items, and perform traffic engineering and resource allocation accordingly. We claim that Information-Centric Networks can therefore provide a better handle to perform traffic engineering, resulting in significant performance gain. We present a mechanism to perform such resource allocation. We see that our traffic engineering method only requires knowledge of the flow size (which, in ICN, can be learned from previous data transfers) and outperforms a min-MLU allocation in terms of response time. We also see that our method identifies the traffic allocation patterns similar to that of min-MLU without having access to the traffic matrix ahead of time. We show a very significant gain in response time where min-MLU is almost 50% slower than our ICN-based TE method.

Most peer-to-peer content distribution systems require the peers to privilege the welfare of the overall system over greedily maximizing their own utility. When downloading a file broken up into multiple pieces, peers are often asked to pass on some possible download opportunities of common pieces in order to favor rare pieces. This is to avoid the missing piece syndrome, which throttles the download rate of the peer-to-peer system to that of downloading the file straight from the server. In other situations, peers are asked to stay in the system even though they have collected all the file's pieces and have an incentive to leave right away. We propose a mechanism which allows peers to act greedily and yet stabilizes the peer-to-peer content sharing system. Our mechanism combines a fountain code at the server to generate innovative new pieces, and a prioritization for the server to deliver pieces only to new peers. While by itself, neither the fountain code nor the prioritization of new peers alone stabilizes the system, we demonstrate that their combination does, through both analytical and numerical evaluation.

The increasing demand for media-rich content has driven many efforts to redesign the Internet architecture. As one of the major candidates, information-centric network (ICN) has attracted significant attention, where in-network cache is a key component in different ICN architectures. In this paper, we propose a novel framework for optimal cache management in ICNs which jointly considers caching strategy and content routing. Specifically, we propose a cache management framework for ICNs based on software-defined networking (SDN) where a controller is responsible for determining the optimal caching strategy and content routing via linear network coding (LNC). Under the proposed cache management framework, we formally formulate the problem of minimizing the network bandwidth cost by jointly considering caching strategy and content routing with LNC. We develop an efficient network coding based cache management (NCCM) algorithm to obtain a near-optimal caching and routing solution for ICNs. We further develop a lower bound of the problem and conduct extensive experiments to compare the performance of the NCCM algorithm with the lower bound. Simulation results validate the effectiveness of the NCCM algorithm and framework.

Information-Centric Networks route based upon content name, and not based upon the locations of the content consumer and producer. However, the current Internet architecture does not allow such functionality since it does not support content caching and routing at the network layer. We present ContentFlow, an Information-Centric network architecture which supports content management and routing by mapping the content name to an IP flow, and thus enables the use of OpenFlow switches to achieve content routing over a legacy IP architecture. ContentFlow extends the OpenFlow infrastructure to insert new network elements (cache and proxy) in addition to switches, and modifies the OpenFlow controller to support content based forwarding primitives. ContentFlow is transparent from the point of view of the client and the server. We present a description of ContentFlow architecture and results from a reference implementation.

Flooding of route requests or link states is a necessity in many routing protocols for mobile ad hoc networks (MANET), and several mechanisms have been devised to make flooding more efficient; however, all flooding approaches to date are such that the number of neighbors each node must use to relay a flooded packet grows as the node density increases. A new method, called ORCA (On-demand Routing with Coordinates Awareness) is introduced for the dissemination of route requests in MANETs. The selection of relaying nodes at each node in ORCA is done by computing the shortest Euclidean Distance from all neighbors of the node to four polar points located in the transmission range of the node. We prove that ORCA guarantees the coverage of all nodes in a connected MANET, and that the number of relays for each node is at most six. ORCA is compared with representative routing protocols, namely AODV, OLSR, LAR, and THP. The simulation results in networks of 200 and 250 nodes show that ORCA incurs the smallest routing load while attaining average delays and packet delivery ratios that are comparable to or better than those obtained with the other four routing protocols.

Monitoring modern networks involves storing and transferring huge amounts of data. To cope with this problem, in this paper we propose a technique that allows to transform the measurement data in a representation format meeting two main objectives at the same time. Firstly, it allows to perform a number of operations directly on the transformed data with a controlled loss of accuracy, thanks to the mathematical framework it is based on. Secondly, the new representation has a small memory footprint, allowing to reduce the space needed for data storage and the time needed for data transfer. To validate our technique, we perform an analysis of its performance in terms of accuracy and memory footprint. The results show that the transformed data closely approximates the original data (within 5% relative error) while achieving a compression ratio of 20%; storage footprint can also be gradually reduced towards the one of the state-of-the-art compression tools, such as bzip2, if higher approximation is allowed. Finally, a sensibility analysis show that technique allows to trade-off the accuracy on different input fields so to accommodate for specific application needs, while a scalability analysis indicates that the technique scales with input size spanning up to three orders of magnitude.

Online social networks (OSNs) play an increasingly important role today in informing users about content. At the same time, mobile devices provide ubiquitous access to this content through the cellular infrastructure. In this paper, we exploit the fact that the interest in content spreads over OSNs, which makes it, to a certain extent, predictable. We propose Proactive Seeding-a technique for minimizing the peak load of cellular networks, by proactively pushing (“seeding”) content to selected users before they actually request it. We develop a family of algorithms that take as input information primarily about (i) cascades on the OSN and possibly about (ii) the background traffic load in the cellular network and (iii) the local connectivity among mobiles; the algorithms then select which nodes to seed and when. We prove that Proactive Seeding is optimal when the prediction of information cascades is perfect. In realistic simulations, driven by traces from Twitter and cellular networks, we find that Proactive Seeding reduces the peak cellular load by 20%-50%. Finally, we combine Proactive Seeding with techniques that exploit local mobile-to-mobile connections to further reduce the peak load.

Online social networks (OSNs) provide new means of disseminating information about applications and contents served by network providers. OSN members often reveal their usage information and opinion about applications and contents to their neighbors within their social networks. Consequently, sudden popularity and viral propagation of applications among OSN members can put significant burden on network resources and degrade network performance. Further, viral exchanges might propagate malicious applications and such propagation might need to be kept in check. Accordingly, we propose a novel content distribution architecture that controls the resource utilization within an operator's network by utilizing the existing social connections between users and building a model of information diffusion within the social network. Our method is based upon computing a reward function that takes into account the influence of users over each other in a given social network in terms of application adoption and content consumption. Based on this reward function and assuming that not only the users but also the network operator can limit the exposure of application usage and content consumption over the online social networks, we present algorithms to slow down or speed up the adoption/consumption of different applications/contents given the current state of both the physical network and the social network. We evaluate the effectiveness of this method over a Flickr data set. Results suggest that such a control is indeed possible and the proposed method significantly outperforms other approaches that employs mainly degree-based mechanisms for controlling the information dissemination over the social graphs.

We present PIE, a scalable routing scheme that achieves 100% packet delivery and low path stretch. It is easy to implement in a distributed fashion and works well when costs are associated to links. Scalability is achieved by using virtual coordinates in a space of concise dimensionality, which enables greedy routing based only on local knowledge. PIE is a general routing scheme, meaning that it works on any graph. We focus however on the Internet, where routing scalability is an urgent concern. We show analytically and by using simulation that the scheme scales extremely well on Internet-like graphs. In addition, its geometric nature allows it to react efficiently to topological changes or failures by finding new paths in the network at no cost, yielding better delivery ratios than standard algorithms. The proposed routing scheme needs an amount of memory polylogarithmic in the size of the network and requires only local communication between the nodes. Although each node constructs its coordinates and routes packets locally, the path stretch remains extremely low, even lower than for centralized or less scalable state-of-the-art algorithms: PIE always finds short paths and often enough finds the shortest paths.

Erasure coding has been proposed in distributed storage systems for both high data reliability and low storage redundancy. With virtualization, virtual machines (VMs) are essentially defined by software and by their memory states, and erasure coding can be used in the same manner as in storage for high reliability and availability. Each VM can backup its memory state to a hot spare, and multiple memory states are coded at the hot spare to provide data reliability and low redundancy. This poses a new set of challenges. Both synchronization and recovery of the VMs consume significant bandwidth which may impede the performance of a data center. To guarantee high availability, recovery from a machine failure (decoding) at the hot spare must be fast. When (de)coding is done in-network, computation can be distributed over hosts, routers or switches and bandwidth for synchronization and recovery can be reduced significantly. We show the conditions needed to support optimal rates for synchronization and recovery for many-to-one backup in arbitrary networks. Furthermore, we propose routing and code construction algorithms that run in polynomial-time.

Rate adaptation plays a central role in the efficiency of data transmissions in wireless networks. Due to the complex physical-layer effects of wireless links, including interference, attenuation, and multi-path fading, designing a rate adaptation algorithm that performs well in most scenarios is a challenging problem. We present the Multi-rate Adaptation with Interference and Congestion Awareness (MAICA) scheme, which is compatible with existing 802.11 implementations and relies only on acknowledgment packets for its operation. Extensive simulations, analytical model, and real-world experiments are used to show that MAICA consistently performs better than prior rate adaptation schemes used to date, especially in dense and congested wireless networks.

Through the recent emergence of joint resource and network virtualization, dynamic composition and provisioning of time-limited and isolated virtual infrastructures is now possible. One other benefit of infrastructure virtualization is the capability of transparent reliability provisioning (reliability becomes a service provided by the infrastructure). In this context, we discuss the motivations and gains of introducing customizable reliability of virtual infrastructures when executing large-scale distributed applications, and present a framework to specify, allocate and deploy virtualized infrastructure with reliability capabilities. An approach to efficiently specify and control the reliability at runtime is proposed. We illustrate these ideas by analyzing the introduction of reliability at the virtual-infrastructure level on a real application. Experimental results, obtained with an actual medical-imaging application running in virtual infrastructures provisioned in the experimental large-scale Grid'5000 platform, show the benefits of the virtualization of reliability.

The number of devices connected to the Internet has outstripped the number of effectively assignable IPv4 addresses. In order to be globally reachable, many devices must share the same IPv4 address; current mechanisms only provide reachability when the device sharing the IPv4 address itself initiates communication. We describe a mechanism to make nodes behind a NAT globally reachable, even when communications are initiated from the global Internet. The intended application of the mechanism, denoted SIPNAT, is to allow for the first time bidirectional global reachability of IPv6 addresses by nodes in the global IPv4 Internet, in a scalable manner, thus resolving the major issue associated with IPv4-IPv6 translation. SIPNAT involves filtering flows at the gateway between the IPv4 and IPv6 domain through a combination of DNS request and timing information for the IPv4 initiated connection (the IPv6 to IPv4 connections are performed using typical NAT mechanisms, where the IPv6 domain takes the role of the private address space). We study the performance of the SIPNAT mechanism using queuing theoretic analysis, and show that our SIPNAT model is accurate on actual data traces.

Virtualization is a key enabler to autonomic management of hosted services in data centers. We show that it can be used to manage reliability of these virtual entities with virtual backups. An architecture is proposed to autonomously manage and allocate the physical resources, ensure reliability guarantees, and manage the pools of virtual backups for failure recovery and resource conservation. It is fault tolerant by design so that component failures do not bring down the entire data center.

How to efficiently use the air interface is a crucial issue in wireless networks. In order to improve the performance, mechanisms have been proposed to improve the reach and the connectivity of nodes in a wireless network. One such mechanism is to use joint, synchronized transmission from a cluster of several nodes to reach nodes outside of the transmission range of any of the transmitting nodes in the cluster. We adopt a simplified model of collaboration where the power at the receiver is the sum of the transmitted power at the sender, and study the performance of such system with clusters of arbitrary size k. We compute theoretical bounds on the gain achieved using collaboration as a function of the cluster size. Our key result is to show that, in a percolation framework, the critical node density for the infinite connectedness of the network is significantly reduced by the use of k-cooperation: for large k, it is reduced by a factor which we show to converge towards alpharadiczeta(alpha)2, where zeta(.) is the Riemann zeta function. The previously known best bound on the gain of cooperation was 5/4 = 1.25 for alpha = 2 and pairwise cooperation, while our results yield a provable gain of at least pi2/6 = 1.64. We provide some simulations to display the gain of cooperation in a finite size network as well.

Barrier coverage of wireless sensor networks has been studied intensively in recent years under the assumption that sensors are deployed uniformly at random in a large area (Poisson point process model). However, when sensors are deployed along a line (e.g., sensors are dropped from an aircraft along a given path), they would be distributed along the line with random offsets due to wind and other environmental factors. It is important to study the barrier coverage of such line- based deployment strategy as it represents a more realistic sensor placement model than the Poisson point process model. This paper presents the first set of results in this direction. In particular, we establish a tight lower-bound for the existence of barrier coverage under line-based deployments. Our results show that the barrier coverage of the line-based deployments significantly outperforms that of the Poisson model when the random offsets are relatively small compared to the sensor's sensing range. We then study sensor deployments along multiple lines and show how barrier coverage is affected by the distance between adjacent lines and the random offsets of sensors. These results demonstrate that sensor deployment strategies have direct impact on the barrier coverage of wireless sensor networks. Different deployment strategies may result in significantly different barrier coverage. Therefore, in the planning and deployment of wireless sensor networks, the coverage goal and possible sensor deployment strategies must be carefully and jointly considered. The results obtained in this paper will provide important guidelines to the deployment and performance of wireless sensor networks for barrier coverage.

We investigate the construction of greedy embeddings in polylogarithmic dimensional Euclidian spaces in order to achieve scalable routing through geographic routing. We propose a practical algorithm which uses random projection to achieve greedy forwarding on a space of dimension O(log(n)) where nodes have coordinates of size O(log(n)), thus achieving greedy forwarding using a route table at each node of polylogarithmic size with respect to the number of nodes. We further improve this algorithm by using a quasi-greedy algorithm which ensures greedy forwarding works along a path-wise construction, allowing us to further reduce the dimension of the embedding. The proposed algorithm, denoted GLoVE-U, is fully distributed and practical to implement. We evaluate the performance using extensive simulations and show that our greedy forwarding algorithm delivers low path stretch and scales properly.

We present an analytical, epidemiological, model for the overhead of information dissemination in mobile networks, which finds application in a range of settings, including resource discovery, information dissemination in sensor networks, location services and ad hoc network routing. We consider two principal information dissemination mechanisms, one for which nodes strictly relay information for other nodes, and the other for which they also take advantage of the information already being disseminated to insert their own data to share. The latter mechanism is denoted by path accumulation. We will show that the overhead with path accumulation scales as n3/2and without path accumulation as n5/4. With respect to the connection rate, both scale as radic(lambda). We also identify the cutoff point for path accumulation as xic= 1 - radic(2/radic(n)). Below that threshold, path accumulation should be preferred, while above, path accumulation does not provide substantial benefits. Our model proves extremely accurate against simulation data.

Extensive research has demonstrated the potential improvement in physical layer performance when multiple radios transmit concurrently in the same radio channel. We consider how such cooperation affects the requirements for full connectivity and percolation in large wireless ad hoc networks. Both noncoherent and coherent cooperative transmission are considered. For one-dimensional (1-D) extended networks, in contrast to noncooperative networks, for any path loss exponent less than or equal to one, full connectivity occurs under the noncoherent cooperation model with probability one for any node density. Conversely, there is no full connectivity with probability one when the path loss exponent exceeds one, and the network does not percolate for any node density if the path loss exponent exceeds two. In two-dimensional (2-D) extended networks with noncoherent cooperation, for any path loss exponent less than or equal to two, full connectivity is achieved for any node density. Conversely, there is no full connectivity when the path loss exponent exceeds two, but the cooperative network percolates for node densities above a threshold which is strictly less than that of the noncooperative network. A less conclusive set of results is presented for the coherent case. Hence, even relatively simple noncoherent cooperation improves the connectivity of large ad hoc networks.

We study the overhead of some mechanisms for information dissemination. We use reactive routing protocols in mobile ad hoc networks as our illustrative example. However, while reactive routing protocols are the main application for our results, they apply to different settings as well, which include publish/subscribe information dissemination mechanisms, information dissemination in sensor networks, and location services. We use a performance measure, defined in [8], which we call the efficiency xi of the information dissemination process, and use this efficiency to evaluate the overhead. The overhead is influenced by the use of a mechanism to increase the amount of information disseminated, at the cost of a higher overhead, called path accumulation. We compare the overhead of routing protocols with and without path accumulation. We observe that path accumulation is always more efficient at disseminating route information in the network, but the gain in efficiency decreases when it is normalized by the number of extra routes discovered, as more is known about the network. This means that the marginal return of path accumulation decreases over not using path accumulation. We propose a new route discovery mechanism which applies to most of the reactive protocols using a route request/route reply exchange. Our route discovery mechanism, denoted adaptive path accumulation adjusts the accumulated path in order to either: scale its bandwidth overhead down as more information is distributed in the network, or improve the network discovery by carefully selecting the routes to be propagated. We assess a range of adaptive path accumulation policies using numerical evaluation and show the effect of these policies on route discovery and packet overhead.

Connectivity and capacity are two measures for the performance of mobile ad hoc networks that have been studied extensively under standard point-to-point physical layer assumptions. However, extensive recent research at the physical layer has demonstrated the improvement in performance possible when multiple radios concurrently transmit in the same radio channel. In this paper, we consider how such physical layer cooperation improves the connectivity in wireless ad hoc networks. In particular, with noncoherent cooperation at the physical layer, we consider conditions on the node density λ (or, equivalently, the transmit power) for full connectivity and percolation for large networks in various dimensions and with various path loss exponents α. For one-dimensional (1-D) extended networks, in sharp contrast to noncooperative networks, we demonstrate that full connectivity can be realized under certain conditions. In particular, for any node density with path loss exponent α < 1, or for node density λ > 2 when α = 1, full connectivity occurs with probability one. Conversely, we demonstrate that, under noncoherent cooperation, there is no full connectivity with probability one when α < 1. In two-dimensional (2-D) extended networks with noncoherent cooperation, for any node density with α < 2, or for node density λ Ū 5 when α = 2, full connectivity is achieved. Conversely, there is no full connectivity with probability one when α > 2, but we prove that, for α ≥ 4, the percolation threshold of the noncoherent cooperative network is strictly less than that of the noncooperative network. Analogous results are presented for dense networks. Hence, the main conclusion is that even relatively simple physical layer cooperation in the form of noncoherent power summing can substantially improve the connectivity of large ad hoc networks.

While the cost of nodes in a wireless mesh network is decreasing, the price tag of the network as a whole is best minimized by deploying the fewest number of nodes that still achieves some target network connectivity. In this document, we study two techniques to improve the connectivity of the network without increasing the density, and thus the cost, of the network. The two techniques are opportunistic routing and collaborative forwarding. Opportunistic routing makes use of links which become temporarily available due to instantaneous radio conditions. Collaborative forwarding forms associations between small connected clusters to forward packets to nodes outside of the range of any node in the cluster. These techniques were introduced by others in earlier papers. However, their evaluation was performed mostly via simulation. The contribution of this paper is to define a mathematical framework using percolation theory to quantify the gain in network coverage achieved using these techniques. This document shows that both technique significantly decrease the critical connectivity threshold for the wireless mesh network, and thus provide significant connectivity gain. This means that fewer nodes are necessary to achieve the same coverage. We focus on analytical techniques to prove the results, and confirm them by performing a numerical evaluation.

The task of routing streaming data in a sensor network is made arduous by the resource constraints imposed on each node. The control overhead of a routing protocol has to be minimized in order to preserve limited resources at the node. Furthermore, it is widely expected that the gateway of the sensor network will not be static in many application scenarios, but will be moving around the network. The gateway could move to collect data, or because it is not associated with a specific location, but with a specific mobile user. We study a simple protocol and consider the cost it imposes on the networks in terms of number of messages sent, or equivalently, bandwidth or power usage. We study the scaling behavior of the so-called bread crumbs (BC) protocol, and show that it is optimal in its scaling behavior in one-and two-dimensional graphs. The BC protocol is thus named as the mobile gateway leaves a trail in the network as it moves about, as Little Tom Thumb, or Hansel and Gretel do in the forest, in the well-known fairy tales. The BC protocol routes packets along the path followed by the mobile sink, taking short cuts whenever possible. We compare this protocol with the cost of routing on the shortest path from the sensor node to the mobile sink, and with the cost of flooding the network in the hope of finding the sink. We show that the path attained with the BC protocol is asymptotically optimal, and scales as the shortest path. We support the analysis with some simulations and also consider a one step diffusion extension of the bread crumbs protocol as well.

Wireless mesh networks are being deployed to provide broadband wireless connectivity to city-wide hotspots. The most successful commercial architecture in these deployments thus far is a single-radio architecture: mesh nodes carry only one radio, which is used both to receive the traffic from the WiFi clients and to relay this traffic through the mesh to the wired internet gateway.

Despite the large amount of research in ad hoc networks, there are still several limitations on the scalability and performance of routing protocols, which require a more fundamental understanding of some of the basic mechanisms. In this paper, we focus on one of these mechanisms, which is route discovery, and analyze its extent and overhead in discovering routes to nodes in the network. Gwalani et al. (2003) has augmented route discovery in AODV with a mechanism called path accumulation, that allows additional routes to be propagated through the network. We provide detailed analysis and simulations for the potential benefits of path accumulation and apply it on one of the prominent ad hoc routing protocols, AODV. The results show that path accumulation can improve the performance of AODV and reduce its overhead significantly.

Routing streaming data in a sensor network is a task made arduous by the resource constraints imposed on each node. Furthermore, it is widely expected that the gateway of the sensor network will not be static in many application scenario, but will be moving about the network. We study a simple protocol and consider the cost it imposes on the networks in terms of number of messages sent, or equivalently, bandwidth or power usage. We study the scaling behavior of the so-called bread crumbs protocol and show that it is optimal in its scaling behavior in one- and two-dimension graphs. We compare this protocol with the cost of routing on the shortest path from the sensor node to the mobile sink, and with the cost of flooding the network in the hope of finding the sink. We positively support the analysis with some simulations of the bread crumbs protocol in both a lattice and a torus scenario.

Sensor networks introduce new constraints on wireless and ad-hoc networks, especially in terms of band-width limitations and power efficiency. If such network is still using IP, for instance if it is connected eventually to the internet, using IP header compression becomes a strong necessity. IP header compression is usually a link compression between the mobile device and the base station. In a sensor network however, there is a succession of wireless links. Link based compression might be costly, as each hop has to decompress the IP headers and re-compress them for further forwarding, consuming power for each operation. We present here a technique to alleviate this issue and provide multi-hop compression. The idea is to split the compression according to the packet's different layers: the network part of the header, and the transport part of the header are compressed differently, using two orthogonal mechanisms. We introduce a signaling that allows the nodes participating in the compression to establish the compression end-to-end. The multi-hop compression might be especially beneficial in sensor networks, where typically payload is small and the cost of transmitting any bit is quite high.

This paper describes the opportunistic routing in dynamic ad hoc networks, the OPRAH protocols, which uses the air interface to find a more optimal path for each packet in a dynamic network. In the static environment the connectivity is perturbed only by fading between two fixed points, the performance of these protocols depends on how steady a route is. Thus these protocols have the following advantages: ability to interoperate with wired protocols, to ensure that the ad hoc network can communicate with the legacy wired infrastructure, it works in a low mobility environment, and it should be implemented using relatively simple off-the-shelf components

Sensor networks (and to some extent ad hoc networks) are networks of nodes with resource constraints, such as a limited battery life, a limited bandwidth or a limited processing capacity. These constraints create a well documented trade-off: each node needs to participate in the network to perform its role or duty, while this participation will deplete the node's resources. Here we consider a particular aspect of this trade-off: the storage of information in the network. Due to the resource constraints, each single node carries an incentive to limit the amount of data it contains. This leads to the expiration of the data carried by the node after a period of time. On the other hand, some data is critical to the functioning of the whole network, and should be found in the network at all times. In C. Westphal (2005), we introduced the trade-off between the finite lifetime of a piece of information at each node, and the survival of this information indefinitely within the network. We then studied this trade-off in a lattice topology and a tree topology. Here, we extend the results of C Westphal (2005) in two directions: we consider random topologies for the underlying network, and we take into account the cost of exchanging information in the network. We show that the maximum number of hops in a request for information broadcast is a critical parameter to ensure the survivability of any information within the network indefinitely. We identify the parameter which minimizes the load on the network for a network graph satisfying the Poisson boolean model. We also show how to minimize the cost of the dissemination on the network, so as to keep this cost decreasing asymptotically to 0

Multi-channel system are becoming more and more available and require new techniques to speed up the channel access, especially when the mobile device is equipped only with a single antenna. In this paper, we introduce some techniques and selection policies which allow a mobile device to speed up the acquisition time for finding out a channel with satisfactory conditions. The 3 policies we introduce all speed up the scanning phase significantly while achieving near optimal throughput. We assess analytically and numerically the performance of the policies and show an acquisition time divided by a factor two to five, while the achieved throughput stays comparable to an exhaustive search .

With the wireless and mobile access to the Internet growing, compression of Internet protocol (IP) headers is seen as crucial to maximally utilize scarce wireless bandwidth. The traditional IP compression schemes rely on consistent per-flow state in order to perform header compression. While these approaches focus on compression gain, i.e., the reduction in the number of header bytes as a percentage of the total packet size, the overhead is the cost of managing state per each unidirectional flow. In this document, we present a new header compression scheme with the emphasis on reducing or even eliminating the state, perhaps at the cost of reduced compression gain compared to the existing methods. We make use of existing route table information in order to achieve the state reduction. The net result is that significant compression gain can be achieved at a greatly reduced cost of maintaining and managing per-flow state information. This could be especially beneficial for short-lived flows.

Ad-Hoc Networks and in particular sensor networks are networks of nodes with limited battery and limited processing capacity. As such, a single node carries an incentive to limit the amount of data it contains. This leads to the expiration of the data carried by the node after a period of time, due for instance, to a re-boot after an off period in the duty cycle, or to older information being "pushed" out by new data received by the mobile node. On the other hand, some data is critical to the functioning of the whole network. For instance, the existence and position of a gateway towards the infrastructure network should be kept somewhere in the network, so that nodes are able to recover this information when needed. In this paper, we study the trade-off between the finite lifetime of a piece of information at each node, and the survival of this information indefinitely within the network. We consider a simple dissemination process for the information akin to an AODV-based information request/reply mechanism. We show that the maximum number of hops in a request is a critical parameter to ensure the survivability indefinitely of any information within the network. We identify the parameter which minimizes the load on the network, for two typical ad-hoc network topologies: a square lattice, which accurately models the distribution of the nodes in a fixed and organized ad hoc or sensor network, and a n-ary tree, which models ad hoc networks for which routing is constructed so as to have no routing loops.

As the 3G cdma2000/sup /spl reg// networks with high data rates get deployed, it is interesting to study the impact of the scheduling algorithms used in these networks, and to improve over their performance. In this paper, we introduce a quantized scheduling that takes into account the fact that mobile nodes and access points can communication only through a finite set of rates or power vectors. This approach allows also to improve over the traditional scheduling algorithm used in the cdma2000 networks, proportional fairness, in terms of fairness in heterogeneous channels (that is channels were the fading is governed by different distributions for different nodes) by mapping all channels to a vector of i.i.d. random variables from which to base the scheduling decision. There is no performance degradation for using this algorithm in Rayleigh fading channels, but improved fairness in the heterogeneous fading case.

As 3G cdma2000/sup /spl reg// networks with high data rates are deployed, it is interesting to study the impact of the scheduling algorithm used in these networks, and to improve its performance. We consider the setting where this scheduling algorithm is used in heterogenous channels, meaning channels for which the fading properties are not the same from one user to the next. Some users might encounter relatively steady conditions while others suffer from fast fading, for instance. In heterogenous channels, it is known that the proportional fairness algorithm falls short of its goal to provide users with a fair throughput that is proportional to their mean rate. We offer an easy way to implement improvements to this shortcoming. The idea for these improvements is to add an independent performance monitoring function to the proportional fairness algorithm that corrects unfairness should it appear.

Due to bandwidth constraints on the wireless link in an IP network, it is useful to compress the headers so as to maximize the utilization of the link. Different header compression schemes have been proposed. Some make use of the similarity in consecutive headers in a packet flow to compress these headers. These are called here time compression, as they acquire the compression information over the time length of a flow. Another scheme introduced recently makes use of the similarity in consecutive flows from or to a given mobile terminal to compress these headers. This we call a space compression, as it uses information about the distribution of flows over the destination address space. We present here a comparison of these two classes of header compression algorithm-time and space- and of a third one that combines them. The combined algorithm significantly outperforms traditional header compression algorithm. We evaluate the compression scheme with respect to actual Internet data traces.

The use of bandwidth constrained links in wireless networks necessitates the use of bandwidth saving header compression schemes. In these schemes, a compressor and a decompressor collaborate to encode bulky IP headers into streamlined compressed headers. Intuitively, the gain from compression is the average compressed header length divided by the uncompressed header size. In this paper, we show that this is not true in many situations: this does not account for the cost introduced by the variability of the header sizes during compression context initialization. First, we provide analytical basis for cost of compression context creation and compare the relative costs of establishing contexts with an optimistic approach and with an acknowledgement-based approach. We conclude that the optimistic approach can be used as an upper bound in our analytical model. Second, we evaluate the impact of header compression on traffic patterns in terms of bandwidth allocation, in order to accommodate the burst introduced by compression context initialization. We compute the actual compression ratio, that is the actual number of users of IP header compression that can be multiplexed onto a given link. We show this number is significantly different from the one computed by dividing the link bandwidth by the average rate of a compressed flow. Our results provide a formal basis for context management especially during handovers in mobile networks. For example, our result indicates that there is significant benefit to relocate compression contexts (from one network node to another) rather than to re-establish them each time during handovers.

Due to bandwidth constraints on the wireless link in an IP network, it is useful to compress the headers so as to maximize the utilization of the link. There exist Header compression algorithms that make use of the similarity in consecutive headers in a packet flow to compress these headers. We introduce a novel header compression scheme that makes use of the similarity in consecutive flows from or to a given mobile terminal to compress these headers. We also introduce an architecture to distribute the information necessary for our header compression scheme to the network entities participating in the header compression by means of context transfer. We evaluate our compression scheme with respect to an analytical model and to actual Internet data traces. Our scheme is complementary and the benefits are additional to the traditional approach to header compression.